A STUDY ON THE INTEGRATION OF DATA LAKES WITH CLOUD COMPUTING PLATFORMS: IMPLICATIONS FOR COST EFFICIENCY AND SCALABILITY

Abstract

The integration of data lakes with cloud computing platforms has become a pivotal strategy for organizations seeking to enhance their data management capabilities while achieving cost efficiency and scalability. Cloud-based data lakes provide a flexible and scalable solution for storing and processing large volumes of diverse data, supported by the elastic resources and advanced services offered by cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud. This paper explores the implications of this integration, focusing on cost efficiency and scalability, two critical factors for organizations leveraging cloud technology. Through a review of existing literature and case studies, the paper examines the cost benefits of cloud- based data lakes, including pay-as-you-go pricing models, storage and processing cost management, and the potential for long-term savings. However, it also addresses the challenges of managing costs in cloud environments, such as unexpected expenses, data egress fees, and the complexities of hybrid and multi-cloud strategies. Additionally, the paper analyzes the scalability advantages of cloud-based data lakes, highlighting the benefits of elastic scaling, performance optimization, and the role of cloud-native architectures in enhancing scalability. It also discusses the challenges of maintaining data governance and ensuring data quality in highly scalable environments. The findings underscore the importance of adopting best practices in cloud architecture, automation, and data governance to maximize the benefits of integrating data lakes with cloud computing platforms. This study provides valuable insights for organizations looking to optimize their cloud-based data lakes for cost efficiency and scalability while navigating the associated challenges.